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    <title>浏阳德塔软件开发有限公司 女娲计划</title>
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    <br/>第一章_德塔自然语言图灵系统
    <br/> 作者: 罗瑶光, Author:Yaoguang.Luo<br/>
    <br/> 基础应用: 元基催化与肽计算 编译机的语言分析机
    <br/>
    神经网络索引 <br/>
    1 德塔分词的词汇字典用map进行索引, 因为Jdk8+的map对象的key支持2分搜索,
    搜索速度到了峰值. refer page, 129, 131 <br/>
    <br/>
    2 德塔分词的索引不断的将大map进行细化分类, 如词长map, 词类map, 词性map,
    让搜索再次加速. refer page 55,<br/>
    <br/>
    3 德塔分词的索引map支持 2次组合计算, 支持分布式服务器进行索引cache.
    关于2次组合计算作者不建议单机使用. refer page 92, <br/>
    <br/>
    4 德塔分词map的key用string的 char对应ASCII int进行标识来执行find key,
    方便二分搜索存储和 StringBuilder高速计算, 实现底层核统一. refer page 92 <br/>
    <br/>
    Nero Network Index Forest <br/>
    1 Deta Parser did a word segmental indexed map by using humanoid
    semantic verbal dictionary, for the reason why using JDK8+ tool to do
    the map search logic, is that It had already integrated the binary
    search tree, balanced map-tree arrangement and other technologies. <br/>
    <br/>
    2 Deta Parser’s balanced binary search tree method made an observer
    mode of averaged classification with all types of the reflection java
    concurrent maps, those maps included the char word length, verbal types
    and part of speech corpus, etc. The author did It to accelerate the
    Nero-marching speedly for searching the words. <br/>
    <br/>
    3 Deta Parser supported the secondary indexing computing combinations,
    this way could be suitable for the distributed cache of searching
    systems. The author did not suggest this technology which be used on a
    single desktop. <br/>
    <br/>
    4 For the computing logic, Finally Deta Parser functions used string
    builder to accelerate the searching engine. <br/>
    <br/>
    神经网络索引的价值主要体现在2个地方, 切词的关联索引上和 词汇map索引上. 切词的关联索引价值,
    主要体现在将词汇的文字进行链化提取, 这种链化计算方式将词库中本相对独立的海量词汇进行了按
    人类语言文学中的顶针方法进行了有效的前后长度关联(NERO), 其价值有利于大文本的文字进行有必
    要关联链的 小段小段的提取(NLP), 类似挤牙膏一样, 挤出来就刷牙用掉(POS). <br/>
    <br/>
    词汇map索引价值, 主要体现在 词汇的文字进行链化合理切分,
    这种链化切分方式将词库中根据不同属性的分类map来组合匹配按人类语言文学中的词汇词性和主谓宾搭配
    严谨定义来切分. 其价值在这些分类map可以自适应设计和多样化扩展. 增加切词准确度和灵活度,
    适应
    各种不同的场景, 类似牙刷机制, 挤出牙膏根据 匹配不同的牙刷和刷牙方法(NERO + POS), 匹配
    适应不同的口腔环境. 描述人 罗瑶光, 稍后优化下. <br/>
    <br/>
    The accomplishment of the neural network-index is mainly reflected in
    two sections, 1 for the relevance index of word segmentation, and 2
    for the lexical indexed map. The associated and relevance-index-value
    of word segmentation, is mainly reflected in the chained extraction of
    words. This chained calculation method effectively correlates the
    relatively independent of a large number of words in the thesaurus,
    according to the Thimble Theory in human language and Literature
    (Nero). The value of the big data document process, splits the word
    chain links list into small chars-token (max 4) sections, and It is
    similar to a squeezing toothpaste, and a brushing teeth (POS) after a
    squeezed out with the DetaParser marching engine.
    <br/>
    The index value of the lexical map is mainly reflected in the
    reasonable chain-segmentation of lexical characters. This chain of word
    segmental method, combines and matches the classified maps in the
    thesaurus, according to different attributes. And then separates them
    according to the rigorous definition of lexical POS and SVO’s
    collocation in human literary languages. The adaptive industrial system
    designed and diversified the expansion of this classification, will
    increase the accuracy and flexibility of word segmentation and adapt to
    different segmental scenes. Similar to the way of toothbrushes, the
    extruded toothpaste is matched to adapt to different oral
    cavity-environments, according to different toothbrushes and brushing
    methods (Nero + POS).
    <br/>
    Author: Yaoguang.Luo <br/>
    <br/>

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